When viruses jump from their natural host into human populations, one of the most pressing questions early in any subsequent epidemic is how the virus will evolve if it cannot be immediately contained and eradicated. In particular, if global pandemics result, or the disease becomes endemic in humans, will it become more or less harmful? Mathematical models of disease ecology and evolution show that when certain key phenotypic determinants of viral fitness are known, it is possible to predict the subsequent direction of virulence evolution. The problem is that these phenotypic details are hard to elucidate. In contrast, advances in molecular biology mean that when cross-species jumps do occur, a deluge of genomic data is generated, allowing genetic tracking of disease evolution. Do these genomic data allow us to predict much about future risk? In this proposal we seek to determine the molecular genetic basis of the evolution of the highly lethal myxoma virus after it was deliberately released as a biocontrol agent against rabbits in both Australia and Europe in the 1950s. These releases were inadvertent experiments in virus evolution, and even today myxoma virus is perhaps the best characterized case of virulence evolution in any vertebrate disease. Critically, the key phenotypic determinants of viral fitness are well characterized, so that the reason natural selection caused changes in myxoma virulence are extremely well known. But the genetic basis of the virulence evolution is not. We will use genomic analysis of viral isolates from both continents, including those sampled in the 1950s, to identify candidate genetic changes responsible for virulence evolution, and then engineer viruses with those mutations. The engineered lines will then be used to determine the causal role of the mutations in the virulence evolution. This work will generate a case study where we can link genotype to phenotype in a context where the transmission ecology is well enough known to predict evolution. Thus, we will be able to assess the power of genomic analysis for predicting future risk.